SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
• Current GRN resources often lack epigenetic data, hindering method development.
• There is a need for comprehensive datasets integrating multiple molecular layers.

Purpose of the Study:

• To develop SC-MO-GRN-DB, a database linking experimentally validated GRNs with single-cell multiomic data.
• To provide a platform for benchmarking and validating GRN inference algorithms.
• To facilitate the study of gene regulation across diverse human and mouse tissues.

Main Methods:

• Curated high-confidence experimental datasets to build ground-truth GRNs.
• Integrated single-cell data from six modalities: scRNA-seq, scATAC-seq, scChIP-seq, scDNA-Met, scHi-C, and scCRISPR-seq.
• Compiled over 22 million regulatory edges and processed more than two million single cells.

Main Results:

• Established SC-MO-GRN-DB, a comprehensive repository of GRNs and multiomic single-cell data.
• Included over 22 million experimentally validated regulatory edges.
• Integrated multiomic data from over two million cells across six molecular modalities.

Conclusions:

• SC-MO-GRN-DB offers a unique resource for advancing GRN research.
• The database enables robust development and validation of GRN inference methods.
• Facilitates deeper understanding of gene regulation by integrating multiomic and epigenetic data.